Title Goes Here
-gayoung Park - Author Two
05 May, 2020
Abstract
Text of abstract
## here() starts at /Users/bmarwick/Desktop/CTtps## ── Attaching packages ── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.0.9000 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## Warning: package 'tibble' was built under R version 3.6.2## ── Conflicts ───── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()1 Introduction
- What are our research questions?
- Understanding technological transition during the Late Pleistocene in Korea base on Cultural transmission.
- What are the social contexts led to the technological transition
What is our archaeological background? -SP was started in Korean Peninsula -> spread to Japan -Why SP important, Why we care -Current explantion of SP dispersal: migration vs in situ, in between The evidence of the two main models: proportion of SP in assemblage, Y chromosome (two groups of people), composition of assemblage (blade to flake ratio) -> no explanation about any mechanism/process/driver for the new technology -> lack of details of the lithic technology, human behavior
What is our conceptual framework?
- Evolutionary concepts that links stone artifacts to human behavior
- Cultural Evolution : the idea that cultural change constitutes an evolutionary process
- Same general concept of biological evolution: genetic change in population that is inherited over generations. Apply this concept to artifacts or cultural phenomena
- In this paper, I will use Cultural Transmission
- Cultural Transmssion: way that people learn and pass on information
- Transmission biases
- similar study (CT in stone artifacts) : Mesoudi&O’Brien, Bettinger&Eerkens, Garvey
- What are our methods?
- Framework: Bettinger&Eerkens
- Two models: Guided variation and Model-based bias => Multi-point Origin (in situ or in between one) and Single-point Origin (migration model)
- Test by Correlation and Variation (Pearson’s r, PCA, Coefficient of Variation)
- data collection, sites, artifact #
- attributes: landmarks (tps software)
- What are our results?
- overall change through time
- correlation: postively correlated(?). We are using PCA too
- PCA: 2,3 phases are highly overlapped, high redundancy. The individual phase are scattered, low redundancy. Variables of Dimension 1 is positively correlated
- CV:values are around 20(ML)-34(SL). Compared to 2nd phase, 3rd phase has slightly lower variation.
- What is the answer to our questions??
- low correlation + high variation? High correlation + low variation?
- What are the criteria for correlation and variation
- How was SP spread in Korea, did they have single orgin or multiple origins?
- What are the impact of combinaton with blade technology
- What is limitation of this study?
Maybe applying CV for SP is not ideal. It can be depending on individual knapper’s skill and raw materials
CV of one site (SYG6) - no big difference with the value of all data
CV: lithis vs pottery
couldn’t include thickness and weight
** Expectation - The main measurements are correlation(+PCA) and CV. - The set of the two measurements would be like: 1) lower CV and higher correlation between sites, in this case, we will think that SPs were the result of model based bias and possibly had single origin. 2) higher CV and lower correlation between sites, for this case, we will thank that SPs were the result of guided variation and possibly had multiple origins. - We have three phases, but there are only two artifacts for the first phase. There are more artifacts and sites for the first phase but not eligible for this research because we gathered complete or almost complete shape of SPs. We excluded broken pieces. Therefore, we focus on the change from the 2nd phase to 3rd phase for understanding the process of SP’s dispersal in the Korean Peninsula. - Change from the 2nd to 3rd 1) less correlated: the two groups were not very related/locally developed 2) highly correlated: the two groups were related, had single source and then spread to other places - Site by site - Regional relationship? Province/cities
2 Background
3 Methods
Reading tps data and phase information of each artifact
Computing size attributes of the points by calculating distance between landmakrs
Gather attributes from all artifacts
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/BG_0_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/GRR_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/HDR_2_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/HDR_2_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/HG_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/HG_0_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/HHGR_0_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/HP1_3b_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/HP1_3b_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/HP2_3b_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/JGN_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/JGN_0_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/JGN_0_3.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/JGN_0_4.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/JJR_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/JM_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/JS_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/MK_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SAR_2_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SAR_2_3.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SAR_3_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SB_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SC_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SC_0_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SJR_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_1_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_1_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_1_3.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_1_4.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_1_5.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_1_6.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_1.TPS"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_10.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_11.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_12.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_13.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_14.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_15.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_16.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_17.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_18.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_19.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_20.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_21.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_22.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_23.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_24.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_25.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_26.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_27.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_28.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_29.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_3.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_30.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_31.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_32.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_33.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_34.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_35.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_4.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_5.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_6.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_7.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_8.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG1_2_9.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_1.TPS"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_10.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_11.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_2.TPS"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_3.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_4.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_5.TPS"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_6.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_7.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_8.TPS"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_3_9.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_10.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_11.TPS"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_12.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_13.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_14.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_15.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_16.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_17.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_18.TPS"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_19.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_20.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_21.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_22.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_23.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_24.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_25.TPS"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_26.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_27.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_28.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_29.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_3.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_30.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_31.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_32.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_33.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_34.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_35.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_36.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_37.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_38.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_39.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_4.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_40.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_41.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_42.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_43.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_44.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_45.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_46.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_47.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_48.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_49.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_5.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_50.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_51.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_52.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_53.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_54.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_55.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_56.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_6.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_7.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_8.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/SYG6_4_9.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/WP_1_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/WP_1_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/WP_1_3.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/WP_2_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/WP_2_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YH_2_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YH_3_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_1.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_10.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_11.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_2.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_3.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_4.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_5.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_6.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_7.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_8.tps"
#> [1] "/Users/bmarwick/Desktop/CTtps//analysis/data/raw_data/YS_0_9.tps"Box plot for each attributes
This boxplot indicates the overview of each attribute. We can see that ML and BL have wider variation compared to other attribtues. Since TL has relativelly less variable, ML variation is more depending on BL. Throughout time and region, dimension of SW is consistant.
Computing Coefficient or variation
#> # A tibble: 1 x 7
#> ML BL TL SL MW TW SW
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 23.8 27.3 34.5 36.3 28.8 31.6 32.0
The coefficient of variation (CV) is the ratio of the standard deviation devided by mean. It shows the extent of variability from the mean.
CV for all attributes are distributed from 23-31%. Interestingly, SL and SW have high CV values (36.3 and 32.0, respectively). These attributes show lower variation on boxplot. <= need to think about
Make a table of CVs for all variable grouped by site
Make a plot for CV by site
The plot shows CV values for different attributes The color of the bars represent different sites. Among tehs sites, we can see SC (Sachang) site has significantly high CV values. SC has two artifacts, one is from 1st phase and the other one is 2nd phase. We assume that this time difference and few number of artifacts may relate to the high CV value.
Unlike the previous plot, in this one, the bars represent different attributes. You can see that SC has highest CV values of most attributes. Overall, SW values are very high.
Make a table of CVs for all variable grouped by phase
This plot shows the difference of CV in between 2nd and 3rd phase. For TL, both two phases have almost same values. For MW and TW, 3rd phase has lower values than the 2nd. The other 4 attributes show higher valeues in 3rd phase, which means less standardized in their shape during the latter period.
By the way, Previous research (Bettinger&Eerkins, Garvey) focused on Width of the projectile points along with weight to understand cultural transmssion. We haven’t figured out which attibutes would be more infortant than others yet.
Computing Pearson’s correlation
=> We may need to delete this chunk since we made new plots below using corrr package and same dataset.
When it comes to the correlations between seven attributes, BL-ML, TW-MW, TL-TW, and TL-SL are relatively close to each other. There is no negative correlation among the attributes. Compared to the 2nd phase, in 3rd phase, correlations of SL with TW and MW, and TL with TWm SL, and MW get stronger. On the other hand, the correlation of TW-SW becomes weeker. Overall, the correlation between attributes became stronger from 2nd to 3rd phase.
Combined the two results of CV and correlation from 2nd to 3rd…
CV: lower in MW and TW, but higher in other 5 attribtues.
COR: overall correlation became stronger.
=> This combination doesn’t fit into the models.
Principal component analysis
Principal Component Analysis is a technique to extract relevant information from a multivariate dataset and to express this information as a set of few new variables called principal components or dimensions (Cascalheira and Bicho, 2018).
In archaeology, PCA has been often used for~
We applied PCA to our dataset to identify correlations between variables that would represent the existence of patterns in the points, which could possilby be translated into more ~ , in addition to correlation analysis.
Contribution of variables for each of four PCA dimensions of ALL phases except for the first.
We see some differences in artefact shape and size. Now we need to examins the PCA output numbers to describe in more details.
Overall, BL and ML have stronger influence than the other attributes and are related to each other but not with the other attributes throughout time. From the phase 2 to the phase 3, the proportion of TL and SL gets bigger and they are more related to SW.
SYG6 site data and analysis: artifacts from SYG6 site: now it has two layers, but the same phase (2nd)
#> # A tibble: 1 x 7
#> ML BL TL SL MW TW SW
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 19.5 23.2 30.5 32.4 18.5 21.2 28.6
SYG1 site data and analysis: artifacts from SYG1 site, which include two differnt locations(excavation pits) and the artifacts are from different phase. SYG1_2 : 3rd phase SYG1_1: 2nd phase
#> # A tibble: 1 x 7
#> ML BL TL SL MW TW SW
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 20.6 22.2 26.8 33.9 18.4 19.0 24.1
#> # A tibble: 1 x 7
#> ML BL TL SL MW TW SW
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21.7 26.4 23.1 35.8 30.9 20.0 36.6
Correlations for each site
In this plot, we can see that the pattern of correlation for SYG6 and SYG1_1 are similar in terms of correlation based on ML/SL/TL with other attributes. Both are part of the phase 2. However, SYG1_2 is younger than the other two and the correlation based on SW become weaker.
When it comes to the difference between SYG6 and SYG1_1, the correlation between artifacts of SYG1_1 is stronger than SYG6.
PCA for each site
Unlike the correlation of the three units, PCA of the three are more various. Especially, SW is related to SL/TL in SYG6, related to ML/BL in SYG1_1, and unrelated to the other attributes in SYG1_2.
#> NULL4 Results
5 Discussion
6 Conclusion
7 Acknowledgements
7.0.0.0.1 pagebreak
8 References
Cascalheira, J., Bicho, N., 2018. The use of lithic assemblages for the definition of short-term occupations in hunter-gatherer prehistory.
8.0.0.0.1 pagebreak
8.0.1 Colophon
This report was generated on 2020-05-05 15:38:12 using the following computational environment and dependencies:
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 3.6.0 (2019-04-26)
#> os macOS 10.15.2
#> system x86_64, darwin15.6.0
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/Los_Angeles
#> date 2020-05-05
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib source
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
#> backports 1.1.6 2020-04-05 [1] CRAN (R 3.6.2)
#> bookdown 0.18 2020-03-05 [1] CRAN (R 3.6.0)
#> broom 0.5.5 2020-02-29 [1] CRAN (R 3.6.0)
#> callr 3.4.3 2020-03-28 [1] CRAN (R 3.6.2)
#> cellranger 1.1.0.9000 2019-05-28 [1] Github (rsheets/cellranger@7ecde54)
#> cli 2.0.2 2020-02-28 [1] CRAN (R 3.6.0)
#> colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.6.0)
#> corrplot * 0.84 2017-10-16 [1] CRAN (R 3.6.0)
#> corrr * 0.4.2 2020-03-22 [1] CRAN (R 3.6.0)
#> crayon 1.3.4.9000 2020-05-03 [1] Github (gaborcsardi/crayon@dcf6d44)
#> DBI 1.1.0 2019-12-15 [1] CRAN (R 3.6.0)
#> dbplyr 1.4.2 2019-06-17 [1] CRAN (R 3.6.0)
#> desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.0)
#> devtools 2.3.0 2020-04-10 [1] CRAN (R 3.6.0)
#> digest 0.6.25 2020-02-23 [1] CRAN (R 3.6.0)
#> dplyr * 0.8.5 2020-03-07 [1] CRAN (R 3.6.0)
#> ellipsis 0.3.0 2019-09-20 [1] CRAN (R 3.6.0)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.0)
#> factoextra * 1.0.7 2020-04-01 [1] CRAN (R 3.6.2)
#> fansi 0.4.1 2020-01-08 [1] CRAN (R 3.6.0)
#> farver 2.0.3 2020-01-16 [1] CRAN (R 3.6.0)
#> forcats * 0.5.0 2020-03-01 [1] CRAN (R 3.6.0)
#> fs 1.4.1 2020-04-04 [1] CRAN (R 3.6.2)
#> generics 0.0.2 2018-11-29 [1] CRAN (R 3.6.0)
#> ggbiplot * 0.55 2019-11-24 [1] Github (vqv/ggbiplot@7325e88)
#> ggfortify * 0.4.9 2020-03-11 [1] CRAN (R 3.6.0)
#> ggplot2 * 3.3.0.9000 2020-05-03 [1] Github (tidyverse/ggplot2@8c66f51)
#> ggpubr 0.2.5 2020-02-13 [1] CRAN (R 3.6.0)
#> ggrepel 0.9.0 2020-04-13 [1] Github (slowkow/ggrepel@3941cf1)
#> ggsignif 0.6.0 2019-08-08 [1] CRAN (R 3.6.0)
#> glue 1.4.0 2020-04-03 [1] CRAN (R 3.6.2)
#> gridExtra * 2.3 2017-09-09 [1] CRAN (R 3.6.0)
#> gtable 0.3.0 2019-03-25 [1] CRAN (R 3.6.0)
#> haven 2.2.0 2019-11-08 [1] CRAN (R 3.6.0)
#> here * 0.1 2017-05-28 [1] CRAN (R 3.6.0)
#> hms 0.5.3 2020-01-08 [1] CRAN (R 3.6.0)
#> htmltools 0.4.0 2019-10-04 [1] CRAN (R 3.6.0)
#> httr 1.4.1 2019-08-05 [1] CRAN (R 3.6.0)
#> jsonlite 1.6.1 2020-02-02 [1] CRAN (R 3.6.0)
#> knitr 1.28 2020-02-06 [1] CRAN (R 3.6.0)
#> labeling 0.3 2014-08-23 [1] CRAN (R 3.6.0)
#> lattice 0.20-41 2020-04-02 [1] CRAN (R 3.6.2)
#> lifecycle 0.2.0 2020-03-06 [1] CRAN (R 3.6.0)
#> lubridate 1.7.8 2020-04-06 [1] CRAN (R 3.6.2)
#> magrittr 1.5 2014-11-22 [1] CRAN (R 3.6.0)
#> MASS 7.3-51.5 2019-12-20 [1] CRAN (R 3.6.0)
#> memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.0)
#> modelr 0.1.6 2020-02-22 [1] CRAN (R 3.6.0)
#> munsell 0.5.0 2018-06-12 [1] CRAN (R 3.6.0)
#> nlme 3.1-147 2020-04-13 [1] CRAN (R 3.6.2)
#> patchwork * 1.0.0 2019-12-01 [1] CRAN (R 3.6.0)
#> pillar 1.4.3 2019-12-20 [1] CRAN (R 3.6.0)
#> pkgbuild 1.0.7 2020-04-25 [1] CRAN (R 3.6.2)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 3.6.0)
#> pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.0)
#> plyr * 1.8.6 2020-03-03 [1] CRAN (R 3.6.0)
#> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 3.6.0)
#> processx 3.4.2 2020-02-09 [1] CRAN (R 3.6.0)
#> ps 1.3.2 2020-02-13 [1] CRAN (R 3.6.0)
#> purrr * 0.3.4 2020-04-17 [1] CRAN (R 3.6.0)
#> R6 2.4.1 2019-11-12 [1] CRAN (R 3.6.0)
#> Rcpp 1.0.4.6 2020-04-09 [1] CRAN (R 3.6.0)
#> readr * 1.3.1 2018-12-21 [1] CRAN (R 3.6.0)
#> readxl 1.3.1 2019-03-13 [1] CRAN (R 3.6.0)
#> remotes 2.1.1 2020-02-15 [1] CRAN (R 3.6.0)
#> reprex 0.3.0 2019-05-16 [1] CRAN (R 3.6.0)
#> rlang 0.4.5 2020-03-01 [1] CRAN (R 3.6.0)
#> rmarkdown 2.1 2020-01-20 [1] CRAN (R 3.6.0)
#> rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.0)
#> rstudioapi 0.11 2020-02-07 [1] CRAN (R 3.6.0)
#> rvest 0.3.5 2019-11-08 [1] CRAN (R 3.6.0)
#> scales * 1.1.0 2019-11-18 [1] CRAN (R 3.6.0)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
#> stringi 1.4.6 2020-02-17 [1] CRAN (R 3.6.0)
#> stringr * 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
#> testthat 2.3.2 2020-03-02 [1] CRAN (R 3.6.0)
#> tibble * 3.0.1 2020-04-20 [1] CRAN (R 3.6.2)
#> tidyr * 1.0.2 2020-01-24 [1] CRAN (R 3.6.0)
#> tidyselect 1.0.0.9000 2020-05-03 [1] Github (r-lib/tidyselect@a63e13d)
#> tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 3.6.0)
#> usethis 1.6.0 2020-04-09 [1] CRAN (R 3.6.0)
#> utf8 1.1.4 2018-05-24 [1] CRAN (R 3.6.0)
#> vctrs 0.2.4 2020-03-10 [1] CRAN (R 3.6.0)
#> withr 2.2.0 2020-04-20 [1] CRAN (R 3.6.2)
#> xfun 0.13 2020-04-13 [1] CRAN (R 3.6.2)
#> xml2 1.3.2 2020-04-23 [1] CRAN (R 3.6.2)
#> yaml 2.2.1 2020-02-01 [1] CRAN (R 3.6.0)
#>
#> [1] /Library/Frameworks/R.framework/Versions/3.6/Resources/libraryThe current Git commit details are:
#> Local: master /Users/bmarwick/Desktop/CTtps
#> Remote: master @ origin (https://github.com/parkgayoung/CTtps)
#> Head: [6709d7e] 2020-04-28: fix namespace clash